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* update * small update * no spqr quant * testing * testing * test nightly * gptqmodel * flute * fix hadamard * running tests * new docker * fix docker * run tests * testing new docker * new docker * run tests * new docker * run tests * final test * update * update * run tests * new docker * launch tests * test_docker * running tests * add comments * fixing yml * revert |
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| .. | ||
| transformers-all-latest-gpu | ||
| transformers-doc-builder | ||
| transformers-gpu | ||
| transformers-past-gpu | ||
| transformers-pytorch-amd-gpu | ||
| transformers-pytorch-deepspeed-amd-gpu | ||
| transformers-pytorch-deepspeed-latest-gpu | ||
| transformers-pytorch-deepspeed-nightly-gpu | ||
| transformers-pytorch-gpu | ||
| transformers-pytorch-tpu | ||
| transformers-quantization-latest-gpu | ||
| transformers-tensorflow-gpu | ||
| consistency.dockerfile | ||
| custom-tokenizers.dockerfile | ||
| examples-tf.dockerfile | ||
| examples-torch.dockerfile | ||
| exotic-models.dockerfile | ||
| jax-light.dockerfile | ||
| pipeline-tf.dockerfile | ||
| pipeline-torch.dockerfile | ||
| quality.dockerfile | ||
| README.md | ||
| tf-light.dockerfile | ||
| torch-jax-light.dockerfile | ||
| torch-light.dockerfile | ||
| torch-tf-light.dockerfile | ||
Dockers for transformers
In this folder you will find various docker files, and some subfolders.
- dockerfiles (ex:
consistency.dockerfile) present under~/dockerare used for our "fast" CIs. You should be able to use them for tasks that only need CPU. For exampletorch-lightis a very light weights container (703MiB). - subfloder contain dockerfiles used for our
slowCIs, which can be used for GPU tasks, but they are BIG as they were not specifically designed for a single model / single task. Thus the~/docker/transformers-pytorch-gpuincludes additional dependencies to allow us to run ALL model tests (saylibrosaortesseract, which you do not need to run LLMs)
Note that in both case, you need to run uv pip install -e ., which should take around 5 seconds. We do it outside the dockerfile for the need of our CI: we checkout a new branch each time, and the transformers code is thus updated.
We are open to contribution, and invite the community to create dockerfiles with potential arguments that properly choose extras depending on the model's dependencies! 🤗